Search results for "Computer Science Application"

Sustainable metabolic engineering for sustainability optimisation of industrial biotechnology

2021

Industrial biotechnology represents one of the most innovating and labour-productive industries with an estimated stable economic growth, thus giving space for improvement of the existing and setting up new value chains. In addition, biotechnology has clear environmental advantages over the chemical industry. Still, biotechnology’s environmental contribution is sometimes valued with controversy and societal aspects are frequently ignored. Environmental, economic and societal sustainability of various bioprocesses becomes increasingly important due to the growing understanding about complex and interlinked consequences of different human activities. Neglecting the sustainability issues in th…

Iterative Reconstruction of Memory Kernels.

2017

In recent years, it has become increasingly popular to construct coarse-grained models with non-Markovian dynamics to account for an incomplete separation of time scales. One challenge of a systematic coarse-graining procedure is the extraction of the dynamical properties, namely, the memory kernel, from equilibrium all-atom simulations. In this article, we propose an iterative method for memory reconstruction from dynamical correlation functions. Compared to previously proposed noniterative techniques, it ensures by construction that the target correlation functions of the original fine-grained systems are reproduced accurately by the coarse-grained system, regardless of time step and disc…

A multistage heuristic for storage and retrieval problems in a warehouse with random storage

2017

The warehouse is one of the essential components of logistics and supply chains. The efficiency of the whole chain is affected by the performance of warehouse operations and, more particularly, the storage and retrieval of goods. This paper considers a storage and retrieval problem in a real warehouse with random storage and different types of forklifts, depending on the locations they can access. The problem deals with selecting locations to store/retrieve a predefined set of pallets, assigning an adequately skilled forklift to each operation and determining the order in which each forklift will perform its operations so that the total employed time is minimized. The problem is solved heur…

A parallel variable neighborhood search approach for the obnoxious p -median problem

2018

On the sure criticality of tasks in activity networks with imprecise durations

2002

BB; International audience; The notion of the necessary criticality (both with respect to path and to activity) of a network with imprecisely defined (by means of intervals or fuzzy intervals) activity duration times is introduced and analyzed. It is shown, in the interval case, that both the problem of asserting whether a given path is necessarily critical and the problem of determining an arbitrary necessarily critical path (more exactly, a subnetwork covering all the necessarily critical. paths) are easy. The corresponding solution algorithms are proposed. However, the problem. of evaluating whether a given activity is necessarily critical does not seem to be such. Certain conditions are…

A biased random-key genetic algorithm for the time-invariant berth allocation and quay crane assignment problem

2017

We address Berth Allocation and Quay Crane Assignment Problems in a heuristic wayWe propose a Biased Random-Key Genetic Algorithm for BACAP and its extension BACASPSolutions of the Genetic Algorithm are improved by a Local SearchThe complete procedure obtains high-quality solutions for large instances Maritime transportation plays a crucial role in the international economy. Port container terminals around the world compete to attract more traffic and are forced to offer better quality of service. This entails reducing operating costs and vessel service times. In doing so, one of the most important problems they face is the Berth Allocation and quay Crane Assignment Problem (BACAP). This pr…

Heuristics for the Bi-Objective Diversity Problem

2018

Abstract The Max-Sum diversity and the Max-Min diversity are two well-known optimization models to capture the notion of selecting a subset of diverse points from a given set. The resolution of their associated optimization problems provides solutions of different structures, in both cases with desirable characteristics. They have been extensively studied and we can find many metaheuristic methodologies, such as Greedy Randomized Adaptive Search Procedure, Tabu Search, Iterated Greedy, Variable Neighborhood Search, and Genetic algorithms applied to them to obtain high quality solutions. In this paper we solve the bi-objective problem in which both models are simultaneously optimized. No pre…

Portfolio optimization using a credibility mean-absolute semi-deviation model

2015

We present a cardinality constrained credibility mean-absolute semi-deviation model.We prove relationships for possibility and credibility moments for LR-fuzzy variables.The return on a given portfolio is modeled by means of LR-type fuzzy variables.We solve the portfolio selection problem using an evolutionary procedure with a DSS.We select best portfolio from Pareto-front with a ranking strategy based on Fuzzy VaR. We introduce a cardinality constrained multi-objective optimization problem for generating efficient portfolios within a fuzzy mean-absolute deviation framework. We assume that the return on a given portfolio is modeled by means of LR-type fuzzy variables, whose credibility dist…

The continuous Berth Allocation Problem in a container terminal with multiple quays

2015

We propose an integer linear model for the case of BAP with multiple quays.We design several constructive procedures and propose a large set of priority rules.We design a genetic algorithm, using the solutions obtained by the priority rules.For BAP with one quay, our genetic algorithm outperforms the best published methods. This paper extends the study of the continuous Berth Allocation Problem to the case of multiple quays, which is found in many container terminals around the world. Considering multiple quays adds a problem of assigning vessels to quays to the problem of determining berthing times and positions for each incoming vessel.This problem has not been considered in the literatur…

Least-squares temporal difference learning based on an extreme learning machine

2014

Abstract Reinforcement learning (RL) is a general class of algorithms for solving decision-making problems, which are usually modeled using the Markov decision process (MDP) framework. RL can find exact solutions only when the MDP state space is discrete and small enough. Due to the fact that many real-world problems are described by continuous variables, approximation is essential in practical applications of RL. This paper is focused on learning the value function of a fixed policy in continuous MPDs. This is an important subproblem of several RL algorithms. We propose a least-squares temporal difference (LSTD) algorithm based on the extreme learning machine. LSTD is typically combined wi…